Open AccessUnveil the time delay signature of optical chaos systems with a convolutional neural network
Author(s) -
Yetao Chen,
Ronghuan Xin,
Mengfan Cheng,
Xiaojing Gao,
Shanshan Li,
Weidong Shao,
Lei Deng,
Minming Zhang,
Songnian Fu,
Deming Liu
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.388182
Subject(s) - convolutional neural network , computer science , signature (topology) , chaos (operating system) , noise (video) , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , feature extraction , artificial neural network , nonlinear system , algorithm , image (mathematics) , physics , mathematics , linguistics , philosophy , geometry , computer security , quantum mechanics
We propose a time delay signature extraction method for optical chaos systems based on a convolutional neural network. Through transforming the time delay signature of a one-dimensional time series into two-dimensional image features, the excellent ability of convolutional neural networks for image feature recognition is fully utilized. The effectiveness of the method is verified on chaos systems with opto-electronic feedback and all optical feedback. The recognition accuracy of the method is 100% under normal conditions. For the system with extremely strong nonlinearity, the accuracy can be 93.25%, and the amount of data required is less than traditional methods. Moreover, it is verified that the proposed method possesses a strong ability to withstand the effects of noise.